论文标题
学习的无损图像压缩，具有高优势和离散的高斯混合物的可能性
Learned Lossless Image Compression with a HyperPrior and Discretized Gaussian Mixture Likelihoods
论文作者
论文摘要
无损图像压缩是多媒体通信领域的重要任务。传统图像编解码器通常支持无损模式，例如WebP，JPEG2000，FLIF。最近，基于深度学习的方法已经开始显示潜力。高位是提出有效图像压缩的有效技术。本文将超级优势从有损模型概括为无损压缩，并提出了L2-norm术语到损失函数中以加快训练程序的速度。此外，本文还研究了潜在代码的不同参数化模型，并建议使用高斯混合物可能性来实现自适应和灵活的上下文模型。实验结果验证我们的方法可以胜过现有的基于深度学习的无损压缩，并且在JPG图像中优于JPEG2000和WebP。
Lossless image compression is an important task in the field of multimedia communication. Traditional image codecs typically support lossless mode, such as WebP, JPEG2000, FLIF. Recently, deep learning based approaches have started to show the potential at this point. HyperPrior is an effective technique proposed for lossy image compression. This paper generalizes the hyperprior from lossy model to lossless compression, and proposes a L2-norm term into the loss function to speed up training procedure. Besides, this paper also investigated different parameterized models for latent codes, and propose to use Gaussian mixture likelihoods to achieve adaptive and flexible context models. Experimental results validate our method can outperform existing deep learning based lossless compression, and outperform the JPEG2000 and WebP for JPG images.
